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C++ Interface: 3 lines of code is all you need to run a YoloX
// create inference engine on gpu-0
//auto engine = Yolo::create_infer("yolov5m.fp32.trtmodel", Yolo::Type::V5, 0);
auto engine = Yolo::create_infer("yolox_m.fp32.trtmodel", Yolo::Type::X, 0);
// load image
auto image = cv::imread("1.jpg");
// do inference and get the result
auto box = engine->commit(image).get(); // return vector<Box>Python Interface:
import pytrt
model = models.resnet18(True).eval().to(device)
trt_model = tp.from_torch(model, input)
trt_out = trt_model(input)
import os
import cv2
import numpy as np
import pytrt as tpengine_file = "yolov5s.fp32.trtmodel" if not os.path.exists(engine_file): tp.compile_onnx_to_file(1, tp.onnx_hub("yolov5s"), engine_file)
yolo = tp.Yolo(engine_file, type=tp.YoloType.V5) image = cv2.imread("car.jpg") bboxes = yolo.commit(image).get() print(f"{len(bboxes)} objects")
for box in bboxes: left, top, right, bottom = map(int, [box.left, box.top, box.right, box.bottom]) cv2.rectangle(image, (left, top), (right, bottom), tp.random_color(box.class_label), 5)
saveto = "yolov5.car.jpg" print(f"Save to {saveto}")
cv2.imwrite(saveto, image) cv2.imshow("result", image) cv2.waitKey()